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Peter Boesiger

Bio: Peter Boesiger is an academic researcher from ETH Zurich. The author has contributed to research in topics: Magnetic resonance imaging & Gastric emptying. The author has an hindex of 91, co-authored 454 publications receiving 31701 citations. Previous affiliations of Peter Boesiger include Philips & École Polytechnique Fédérale de Lausanne.


Papers
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PatentDOI
TL;DR: The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k‐space sampling patterns and special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density.
Abstract: The invention relates to a method of parallel imaging for obtaining images by means of magnetic resonance (MR). The method includes the simultaneous measurement of sets of MR singals by an array of receiver coils, and the reconstruction of individual receiver coil images from the sets of MR signals. In order to reduce the acquisition time, the distance between adjacent phase encoding lines in k-space is increased, compared to standard Fourier imaging, by a non-integer factor smaller than the number of receiver coils. This undersampling gives rise to aliasing artifacts in the individual receiver coil images. An unaliased final image with the same field of view as in standard Fourier imaging is formed from a combination of the individual receiver coil images whereby account is taken of the mutually different spatial sensitivities of the receiver coils at the positions of voxels which in the receiver coil images become superimposed by aliasing. This requires the solution of a linear equation by means of the generalised inverse of a sensitivity matrix. The reduction of the number of phase encoding lines by a non-integer factor compared to standard Fourier imaging provides that different numbers of voxels become superimposed (by aliasing) in different regions of the receiver coil images. This effect can be exploited to shift residual aliasing artifacts outside the area of interest.

6,562 citations

Journal ArticleDOI
TL;DR: Using the proposed method, SENSE becomes practical with nonstandard k‐space trajectories, enabling considerable scan time reduction with respect to mere gradient encoding, and the in vivo feasibility of non‐Cartesian SENSE imaging with iterative reconstruction is demonstrated.
Abstract: New, efficient reconstruction procedures are proposed for sensitivity encoding (SENSE) with arbitrary k-space trajectories. The presented methods combine gridding principles with so-called conjugate-gradient iteration. In this fashion, the bulk of the work of reconstruction can be performed by fast Fourier transform (FFT), reducing the complexity of data processing to the same order of magnitude as in conventional gridding reconstruction. Using the proposed method, SENSE becomes practical with nonstandard k-space trajectories, enabling considerable scan time reduction with respect to mere gradient encoding. This is illustrated by imaging simulations with spiral, radial, and random k-space patterns. Simulations were also used for investigating the convergence behavior of the proposed algorithm and its dependence on the factor by which gradient encoding is reduced. The in vivo feasibility of non-Cartesian SENSE imaging with iterative reconstruction is demonstrated by examples of brain and cardiac imaging using spiral trajectories. In brain imaging with six receiver coils, the number of spiral interleaves was reduced by factors ranging from 2 to 6. In cardiac real-time imaging with four coils, spiral SENSE permitted reducing the scan time per image from 112 ms to 56 ms, thus doubling the frame-rate. Magn Reson Med 46:638–651, 2001. © 2001 Wiley-Liss, Inc.

1,221 citations

Journal ArticleDOI
TL;DR: Based on this approach, two methods were developed to significantly improve the performance of dynamic imaging, named k‐t BLAST (Broad‐use Linear Acquisition Speed‐up Technique) and k-t SENSE (SENSitivity Encoding) for use with a single or multiple receiver coils, respectively.
Abstract: Dynamic images of natural objects exhibit significant correlations in k-space and time. Thus, it is feasible to acquire only a reduced amount of data and recover the missing portion afterwards. This leads to an improved temporal resolution, or an improved spatial resolution for a given amount of acquisition. Based on this approach, two methods were developed to significantly improve the performance of dynamic imaging, named k-t BLAST (Broad-use Linear Acquisition Speed-up Technique) and k-t SENSE (SENSitivity Encoding) for use with a single or multiple receiver coils, respectively. Signal correlations were learned from a small set of training data and the missing data were recovered using all available information in a consistent and integral manner. The general theory of k-t BLAST and k-t SENSE is applicable to arbitrary k-space trajectories, timevarying coil sensitivities, and under- and overdetermined reconstruction problems. Examples from ungated cardiac imaging demonstrate a 4-fold acceleration (voxel size 2.42 2.52 mm 2 , 38.4 fps) with either one or six receiver coils. k-t BLAST and k-t SENSE are applicable to many areas, especially those exhibiting quasiperiodic motion, such as imaging of the heart, the lungs, the abdomen, and the brain under periodic stimulation. Magn Reson Med 50:1031–1042, 2003. © 2003 Wiley-Liss, Inc.

886 citations

Journal ArticleDOI
TL;DR: Given sufficient data sparsity and base signal‐to‐noise ratio (SNR), CS is demonstrated to result in improved temporal fidelity compared to k‐t BLAST reconstructions for the example data sets used in this work.
Abstract: Recent theoretical advances in the field of compressive sampling-also referred to as compressed sensing (CS)-hold considerable promise for practical applications in MRI, but the fundamental condition of sparsity required in the CS framework is usually not fulfilled in MR images. However, in dynamic imaging, data sparsity can readily be introduced by applying the Fourier transformation along the temporal dimension assuming that only parts of the field-of-view (FOV) change at a high temporal rate while other parts remain stationary or change slowly. The second condition for CS, random sampling, can easily be realized by randomly skipping phase-encoding lines in each dynamic frame. In this work, the feasibility of the CS framework for accelerated dynamic MRI is assessed. Simulated datasets are used to compare the reconstruction results for different reduction factors, noise, and sparsity levels. In vivo cardiac cine data and Fourier-encoded velocity data of the carotid artery are used to test the reconstruction performance relative to k-t broad-use linear acquisition speed-up technique (k-t BLAST) reconstructions. Given sufficient data sparsity and base signal-to-noise ratio (SNR), CS is demonstrated to result in improved temporal fidelity compared to k-t BLAST reconstructions for the example data sets used in this work.

587 citations

Journal ArticleDOI
TL;DR: Left-right DLPFC imbalance is characterized in neuropsychological regard, which bridges the gap from resting metabolism and therapeutic repetitive transcranial magnetic stimulation effects to functional neuroanatomy of altered emotional-cognitive interaction in MDD.

525 citations


Cited by
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Journal ArticleDOI
TL;DR: Practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference and demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin‐echo brain imaging and 3D contrast enhanced angiography.
Abstract: The sparsity which is implicit in MR images is exploited to significantly undersample k -space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial finite-differences or their wavelet coefficients. According to the recently developed mathematical theory of compressedsensing, images with a sparse representation can be recovered from randomly undersampled k -space data, provided an appropriate nonlinear recovery scheme is used. Intuitively, artifacts due to random undersampling add as noise-like interference. In the sparse transform domain the significant coefficients stand out above the interference. A nonlinear thresholding scheme can recover the sparse coefficients, effectively recovering the image itself. In this article, practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference. Incoherence is introduced by pseudo-random variable-density undersampling of phase-encodes. The reconstruction is performed by minimizing the 1 norm of a transformed image, subject to data

6,653 citations

Journal ArticleDOI
TL;DR: This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD‐AUTO‐SMASH reconstruction techniques and provides unaliased images from each component coil prior to image combination.
Abstract: In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique.

5,022 citations

Journal ArticleDOI
01 Dec 1999-JAMA
TL;DR: The functional regulation of the endothelium by local hemodynamic shear stress provides a model for understanding the focal propensity of atherosclerosis in the setting of systemic factors and may help guide future therapeutic strategies.
Abstract: Atherosclerosis, the leading cause of death in the developed world and nearly the leading cause in the developing world, is associated with systemic risk factors including hypertension, smoking, hyperlipidemia, and diabetes mellitus, among others. Nonetheless, atherosclerosis remains a geometrically focal disease, preferentially affecting the outer edges of vessel bifurcations. In these predisposed areas, hemodynamic shear stress, the frictional force acting on the endothelial cell surface as a result of blood flow, is weaker than in protected regions. Studies have identified hemodynamic shear stress as an important determinant of endothelial function and phenotype. Arterial-level shear stress (>15 dyne/cm2) induces endothelial quiescence and an atheroprotective gene expression profile, while low shear stress (<4 dyne/cm2), which is prevalent at atherosclerosis-prone sites, stimulates an atherogenic phenotype. The functional regulation of the endothelium by local hemodynamic shear stress provides a model for understanding the focal propensity of atherosclerosis in the setting of systemic factors and may help guide future therapeutic strategies.

3,246 citations

Journal ArticleDOI
12 Jun 2008-Nature
TL;DR: An overview of the current state of fMRI is given, and the current understanding of the haemodynamic signals and the constraints they impose on neuroimaging data interpretation are presented.
Abstract: Functional magnetic resonance imaging (fMRI) is currently the mainstay of neuroimaging in cognitive neuroscience. Advances in scanner technology, image acquisition protocols, experimental design, and analysis methods promise to push forward fMRI from mere cartography to the true study of brain organization. However, fundamental questions concerning the interpretation of fMRI data abound, as the conclusions drawn often ignore the actual limitations of the methodology. Here I give an overview of the current state of fMRI, and draw on neuroimaging and physiological data to present the current understanding of the haemodynamic signals and the constraints they impose on neuroimaging data interpretation.

3,075 citations